The commissioning of HVAC ductworks, particularly the Testing, Adjusting, and Balancing (TAB), is an iterative, time-consuming, and complex process. Traditional research has focused on developing simulation-based methods to automate this process, which in theory should replicate the planned behavior of ductwork systems. However, these simulations often fail to accurately reflect installed systems due to inevitable variations in installation and discrepancies between design specifications and actual installations. This misalignment significantly challenges accurate prediction and optimization of aeraulic performance using simulation tools alone. This study critically evaluates the limitations of current commissioning methodologies in addressing the discrepancies between designed and installed HVAC systems. Utilizing a K-equivalent modeling approach within a Python-based simulation framework, the research quantifies the impacts of layout alterations and installation deviations on system performance. The findings highlight the gap between simulation predictions and actual performance, emphasizing the need for more sophisticated and layout adaptable simulation tools. In response, this study proposes a hybrid balancing strategy that combines model-based constraints with data-driven adaptability to better accommodate real-world complexities. Future research should focus on developing an adaptive aeraulic solver capable of real-time ductwork topology reconstruction and performance prediction. Such advancements aim to enhance commissioning accuracy and streamline the adjustment process, thereby reducing the time and cost associated with achieving optimal HVAC system performance.